The training pipelines for the RNN with the P-FDB loss and the F-FDB loss are shown in Figs. REF  and REF , respectively. The RNN takes in two images together, namely, the previous stylized frame \(\tilde{I}_t\)  and the current content frame \(I_{t+1}\) . These are concatenated along the channel axis and then fed into the RNN. Except for the difference in the input layer, the other layers of the RNN are the same as the SFN. Since the “previous” stylization frame of the first frame does not exist, it is set to a matrix of all zeros. The temporal loss (REF ) is calculated based on \(I_{t}, I_{t+1},\tilde{I}_{t}\)  and \(\tilde{I}_{t+1}\) .
In training and inference, the model works in the same manner. Replacing the FDB loss with the OFB loss transforms the model back to the model in [1]}.
